ASSESSMENT OF DEFORMATION PROPERTIES OF COAL MEASURE SANDSTONES THROUGH REGRESSION ANALYSES AND ARTIFICIAL NEURAL NETWORKS

KÖKEN E.

ARCHIVES OF MINING SCIENCES, cilt.66, sa.4, ss.523-542, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.24425/ams.2021.139595
  • Dergi Adı: ARCHIVES OF MINING SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source
  • Sayfa Sayıları: ss.523-542
  • Anahtar Kelimeler: Sandstone, Zonguldak, deformation properties, regression analysis, artificial neural network, UNIAXIAL COMPRESSIVE STRENGTH, CRACK INITIATION STRESS, TEXTURAL CHARACTERISTICS, ENGINEERING PROPERTIES, MECHANICAL-PROPERTIES, GRANITIC-ROCKS, GRAIN-SIZE, QUANTITATIVE ASSESSMENT, FUZZY INFERENCE, HULU LANGAT
  • Abdullah Gül Üniversitesi Adresli: Evet

Özet

The deformation properties of rocks play a crucial role in handling most geomechanical problems. However, the determination of these properties in laboratory is costly and necessitates special equipment. Therefore, many attempts were made to estimate these properties using different techniques. In this study, various statistical and soft computing methods were employed to predict the tangential Young Modulus (Eti, GPa) and tangential Poisson's Ratio (vti) of coal measure sandstones located in Zonguldak Hardcoal Basin (ZHB), NW Turkey. Predictive models were established based on various regression and artificial neural network (ANN) analyses, including physicomechanical, mineralogical, and textural properties of rocks. The analysis results showed that the mineralogical features such as the contents of quartz (Q, %) and lithic fragment (LF, %) and the textural features (i.e., average grain size, d50, and sorting coefficient, Sc) have remarkable impacts on deformation properties of the investigated sandstones. By comparison with these features, the mineralogical effects seem to be more effective in predicting the Eti and vti. The performance of the established models was assessed using several statistical indicators. The predicted results from the proposed models were compared to one another. It was concluded that the empirical models based on the ANN were found to be the most convenient tools for evaluating the deformational properties of the investigated sandstones.